Abstract
A novel method based on the Vancouver Raman algorithm (VRA) and
empirical mode decomposition (EMD) for denoising Raman spectra of
biological samples is presented. The VRA is one of the most used methods
for denoising Raman spectroscopy and is composed of two main steps: signal
filtering and polynomial fitting. However, the signal filtering step
consists in a simple mean filter that could eliminate spectrum peaks with
small intensities or merge relatively close spectrum peaks into one single
peak. Thus, the result is often sensitive to the order of the mean filter,
so the user must choose it carefully to obtain the expected result; this
introduces subjectivity in the process. To overcome these disadvantages,
we propose a new algorithm, namely the modified-VRA (mVRA) with the
following improvements: (1) to replace the mean filter step by EMD as an
adaptive parameter-free signal processing method; and (2) to automate the
selection of polynomial degree. The denoising capabilities of VRA, EMD,
and mVRA were compared in Raman spectra of artificial data based on Teflon
material, synthetic material obtained from vitamin E and paracetamol, and
biological material of human nails and mouse brain. The correlation
coefficient (ρ) was used to compare the performance of the methods.
For the artificial Raman spectra, the denoised signal obtained by mVRA
(ρ>0.91) outperforms VRA (ρ>0.86) for moderate
to high noise levels whereas mVRA outperformed EMD (ρ>0.90)
for high noise levels. On the other hand, when it comes to modeling the
underlying fluorescence signal of the samples (i.e., the baseline trend),
the proposed method mVRA showed consistent results (ρ>0.94).
For Raman spectra of synthetic material, good performance of the three
methods (ρ=0.99 for VRA, ρ=0.93 for EMD, and ρ=0.99
for mVRA) was obtained. Finally, in the biological material, mVRA and VRA
showed similar results (ρ=0.96 for VRA, ρ=0.85 for EMD, and
ρ=0.91 for mVRA); however, mVRA retains valuable information
corresponding to relevant Raman peaks with small amplitude. Thus, the
application of EMD as a filter in the VRA method provides a good
alternative for denoising biological Raman spectra, since the information
of the Raman peaks is conserved and parameter tuning is not required.
Simultaneously, EMD allows the baseline correction to be
automated.
© 2019 The Author(s)
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